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Thursday, March 21, 2019

NVIDIA Research has demonstrated GauGAN, a deep learning model that converts simple doodles into photorealistic images. The tool crafts images nearly instantaneously, and can intelligently adjust elements within images, such as adding reflections to a body of water when trees or mountains are placed near it.

The new tool is made possible using generative adversarial networks called GANs. With GauGAN, users select image elements like 'snow' and 'sky,' then draw lines to segment an image into different elements. The AI automatically generates the appropriate image for that element, such as a cloudy sky, grass, and trees.

As NVIDIA reveals in its demonstration video, GauGAN maintains a realistic image by dynamically adjusting parts of the render to match new elements. For example, transforming a grassy field to a snow-covered landscape will result in an automatic sky change, ensuring the two elements are compatible and realistic.

GauGAN was trained using millions of images of real environments. In addition to generating photorealistic landscapes, the tool allows users to apply style filters, including ones that give the appearance of sunset or a particular painting style. According to NVIDIA, the technology could be used to generate images of other environments, including buildings and people.

Talking about GauGAN is NVIDIA VP of applied deep learning research Bryan Catanzaro, who explained:

This technology is not just stitching together pieces of other images, or cutting and pasting textures. It's actually synthesizing new images, very similar to how an artist would draw something.

NVIDIA envisions a tool based on GauGAN could one day be used by architects and other professionals who need to quickly fill a scene or visualize an environment. Similar technology may one day be offered as a tool in image editing applications, enabling users to add or adjust elements in photos.

The company offers online demos of other AI-based tools on its AI Playground.

Friday, February 15, 2019

The ability of AI to generate fake visuals is not yet mainstream knowledge, but a new website — ThisPersonDoesNotExist.com — offers a quick and persuasive education.

The site is the creation of Philip Wang, a software engineer at Uber, and uses research released last year by chip designer Nvidia to create an endless stream of fake portraits. The algorithm behind it is trained on a huge dataset of real images, then uses a type of neural network known as a generative adversarial network (or GAN) to fabricate new examples.

“Each time you refresh the site, the network will generate a new facial image from scratch,” wrote Wang in a Facebook post. He added in a statement to Motherboard: “Most people do not understand how good AIs will be at synthesizing images in the future.”

The underlying AI framework powering the site was originally invented by a researcher named Ian Goodfellow. Nvidia’s take on the algorithm, named StyleGAN, was made open source recently and has proven to be incredibly flexible. Although this version of the model is trained to generate human faces, it can, in theory, mimic any source. Researchers are already experimenting with other targets. including anime characters, fonts, and graffiti.

Thursday, February 14, 2019

The future of work is usually discussed in theoretical terms. Reports and opinion pieces cover the full spectrum of opinion, from the dystopian landscape that leaves millions unemployed, to new opportunities for social and economic mobility that could transform society for the better.

The World Economic Forum’s The Future of Jobs 2018 aims to base this debate on facts rather than speculation. By tracking the acceleration of technological change as it gives rise to new job roles, occupations and industries, the report evaluates the changing contours of work in the Fourth Industrial Revolution.

One of the primary drivers of change identified is the role of emerging technologies, such as artificial intelligence (AI) and automation. The report seeks to shed more light on the role of new technologies in the labour market, and to bring more clarity to the debate about how AI could both create and limit economic opportunity. With 575 million members globally, LinkedIn’s platform provides a unique vantage point into global labour-market developments, enabling us to support the Forum's examination of the trends that will shape the future of work.

Our analysis uncovered two concurrent trends: the continued rise of tech jobs and skills, and, in parallel, a growth in what we call “human-centric” jobs and skills. That is, those that depend on intrinsically human qualities.

Thursday, January 10, 2019

Walker is one of newest robots from UBTECH Robotics. Below is just a few of the features and technologies used in its development.

1.Flexible walking on complex terrain: With gait planning and control, Walker can achieve stable walking on different surfaces including carpet, floor, marble, and more. Walker can also adapt to complex environments such as obstacles, slopes, steps, and uneven ground.

2.Self-balancing: When Walker is disturbed by external impact or inertia, it can automatically adjust its center of gravity to maintain balance.

3.Hand-eye coordination: Walker’s hands offer seven degrees of freedom to flexibly manipulate objects. By combining its hands with its own perception, Walker can also position dynamic external objects while adapting to uncertain conditions in real-time.

6.Smart home control: Walker can help users control common household equipment such as lighting, electrical appliances and electrical sockets, enhancing safety, convenience, and comfort.

With so much innovative technology packed into its humanoid robot body, Walker has the intelligence and capabilities to make a helpful impact in any home or business in the very near future.

Founded in 2012, UBTECH is a global leading AI and humanoid robotic company. In 2018, UBTECH achieved a valuation of USD$5 billion following the single largest funding round ever for an artificial intelligence company, underscoring the company’s technological leadership.

Hexbot just launched on Kickstarter, but has already reached more than three times the $50,000 funding goal. It’s easy to see why; Hexbot is a small, but capable, modular robot arm that costs just $299 through the Kickstarter Special. That price puts it near the bottom of the market, but it has the kinds of features and specs you’d normally only find on mid-level robot arms.

A team of researchers has stumbled on a question that is mathematically unanswerable because it is linked to logical paradoxes discovered by Austrian mathematician Kurt Gödel in the 1930s that can’t be solved using standard mathematics.

The mathematicians, who were working on a machine-learning problem, show that the question of ‘learnability’ — whether an algorithm can extract a pattern from limited data — is linked to a paradox known as the continuum hypothesis. Gödel showed that the statement cannot be proved either true or false using standard mathematical language. The latest result appeared on 7 January in Nature Machine Intelligence1.

“For us, it was a surprise,” says Amir Yehudayoff at the Technion–Israel Institute of Technology in Haifa, who is a co-author on the paper. He says that although there are a number of technical maths questions that are known to be similarly ‘undecidable’, he did not expect this phenomenon to show up in a relatively simple problem in machine learning.

John Tucker, a computer scientist at Swansea University, UK, says that the paper is “a heavyweight result on the limits of our knowledge”, with foundational implications for both mathematics and machine learning.

SIMPLE Descriptors

A set of local image descriptors specifically designed for image retrieval tasks.

Compact Composite Descriptors

A set of global image descriptors for image retrieval tasks.

MPEG-7 Descriptors

Download the latest Version of MPEG-7 Descriptors for C#. The implementation of these descriptors is based on Lire image retrieval System (Lire). Download the Descriptors

The LIRE (Lucene Image REtrieval) library provides a simple way to retrieve images and photos based on their color and texture characteristics. LIRE creates a Lucene index of image features for content based image retrieval (CBIR). Three of the available image features are taken from the MPEG-7 Standard: ScalableColor, ColorLayout and EdgeHistogram a fourth one, the Auto Color Correlogram has been implemented based on recent research results. Furthermore simple methods for searching the index and result browsing are provided by LIRE. The LIRE library and the LIRE Demo application as well as all the source are available under the Gnu GPL license.

Img(Rummager)

Img(Rummager) software can be connected with a database and execute a retrieval procedure, extracting the necessary for the comparison features in real time. The image-database can be stored either in the computer where the retrieval is actually taking place, or in a local network. Moreover, this software is capable of executing retrieval procedure among the keyword-based results that FlickR provides. Read More

Several image processing and retrieval examples using c#

Caliph & Emir
Caliph & Emir are MPEG-7 based Java prototypes for digital photo and image annotation and retrieval supporting graph like annotation for semantic metadata and content based image retrieval using MPEG-7 descriptors
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